Hybrid PSO-SA Approach for Feature Weighting in Analogy-Based Software Project Effort Estimation | ||
| Journal of AI and Data Mining | ||
| دوره 9، شماره 3، مهر 2021، صفحه 329-340 اصل مقاله (1.39 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2021.10119.2152 | ||
| نویسندگان | ||
| Z. Shahpar1؛ V. Khatibi2؛ A. Khatibi Bardsiri2 | ||
| 1Department of Computer engineering, Kerman Branch, Islamic Azad University, Kerman, Iran. | ||
| 2Department of Computer engineering, Bardsir Branch, Islamic Azad University, Bardsir, Iran. | ||
| چکیده | ||
| Software effort estimation plays an important role in software project management, and analogy-based estimation (ABE) is the most common method used for this purpose. ABE estimates the effort required for a new software project based on its similarity to previous projects. A similarity between the projects is evaluated based on a set of project features, each of which has a particular effect on the degree of similarity between projects and the effort feature. The present study examines the hybrid PSO-SA approach for feature weighting in analogy-based software project effort estimation. The proposed approach was implemented and tested on two well-known datasets of software projects. The performance of the proposed model was compared with other optimization algorithms based on MMRE, MDMRE, and PRED(0.25) measures. The results showed that weighted ABE models provide more accurate and better effort estimates relative to unweighted ABE models and that the PSO-SA hybrid approach has led to better and more accurate results compared with the other weighting approaches in both datasets. | ||
| کلیدواژهها | ||
| Software effort estimation؛ Analogy based estimation؛ Feature weight optimization؛ Particle swarm optimization؛ Simulated Annealing | ||
| مراجع | ||
|
[1] M. Shepperd and C. Schofield, “Estimating software project effort using analogies,” IEEE Transactions on Software Engineering, vol. 23, no. 11, pp. 736-743, 1997.
[2] I. Guyon and A. Elisseeff “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003.
[3] D. B. Skalak, “Prototype and feature selection by sampling and random mutation hill climbing algorithms,” In 11th International Machine Learning Conference, ICML-94, Morgan Kau_mann, pp. 293-301, 1994.
[4] Z. Shahpar et al., “Improvement of effort estimation accuracy in software projects using a feature selection approach,” Journal of Advances in Computer Engineering and Technology, vol. 2, pp. 31-38, 2016.
[5] E. Papatheocharous et al., “Feature Subset Selection for Software Cost Modelling and Estimation,” Engineering Intelligent Systems, vol. 18, 2010.
[6] B. B. Sigweni, “An Investigation of Feature Weighting Algorithms and Validation Techniques using Blind Analysis for Analogy-based Estimation,” Ph.D. dissertation, Brunel Univ., London, 2016.
[7] A. Kolcz and Y. Wen-tau “Raising the baseline for high-precision text classifiers,” Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 400-409, 2007.
[8] Y. Saeys, I. Inza and P. Larranaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007.
[9] D. Koller and M. Sahami “Toward optimal feature selection,” Tech. Rep. TR-1996-77, Stanford InfoLab. 1996.
[10] J. W. Keung and B. Kitchenham, “Optimizing Project Feature Weights for Analogy-based Software Cost Estimation using the Mantel Correlation,” IEEE, 14th Asia-Pacific Software Engineering Conference, Aichi, pp. 222-229, 2007.
[11] J. W. Keung, A. Kitchenham and D. R. Jeffery, “Analogy-X: Providing Statistical Inference to Analogy-Based Software Cost Estimation,” IEEE Transactions on Software Engineering, vol. 34, no. 4, pp. 471-484, 2008.
[12] J. Wen, S. Li. and L. Tang, “Improve Analogy-based Software Effort Estimation using Principal Components Analysis and Correlation Weighting,” 16th Asia-Pacific Software Engineering Conference, pp. 179-186, 2009.
[13] E. Khatibi and V. Khatibi, “Model to estimation the software development effort based on in-depth analysis of project attributes,” The Institution of Engineering and Technology, vol. 9, pp. 109-118, 2015.
[14] J. Li and G. Ruhe, “Software effort estimation by analogy using attribute selection based on rough set analysis,” International Journal of Software Engineering and Knowledge Engineering, vol. 18, no. 1, pp. 1-23, 2008.
[15] J.Li and G. Ruhe, “Analysis of attribute weighting heuristics for analogy based software effort estimation method AQUA+,” Empirical Software Engineering, vol. 13, no. 1, pp. 63-96, 2008.
[16] Y. F. Li, M. Xie, and T. N. Goh, “A study of project selection and feature weighting for analogy based software cost estimation,” The Journal of Systems and Software, vol. 82, no. 2, pp. 241-252, 2009.
[17] Y. F. Li, M. Xie, and T. N. Goh, “A study of genetic algorithm for project selection for analogy based software cost estimation,” IEEE, International Conference on Industrial Engineering and Engineering Management, Singapore, pp. 1256-1260, 2007.
[18] S. J. Huang and N. H. Chiu, “Optimization of Analogy Weights by Genetic Algorithm for Software Effort Estimation,” Information and Software technology, vol. 48, pp. 1034-1045, 2006.
[19] Y. F. Li, M. Xie, and T. N. Goh, “Optimization of feature weights and number of neighbors for Analogy based cost Estimation in software project management,” IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, pp. 1542-1546, 2008.
[20] A. L. I. Oliveira et al., “GA-based method for feature selection and parameters optimization for machine learning regression applied to software effort estimation,” Information and Software Technology, vol. 52, no. 11, pp. 1155-1166, 2010.
[21] V. Khatibi et al., “A hybrid method for increasing the accuracy of software development effort estimation,” Scientific Research and Essays, vol. 6, no. 30, pp. 6382-6382, 2011.
[22] P. Reddy, C. Hari and S. Rao “Multi- Objective Particle Swarm Optimization for Software Cost Estimation,” International Journal of Computer Applications, vol. 32, no. 3, pp. 13-17, 2011.
[23] M. Azzeh et al., “Pareto efficient multi-objective optimization for local tuning of analogy-based estimation,” Springer, Neural Comput & Applic, November, vol. 27, no. 8, pp. 2241-2265, 2015.
[24] R. D. A. Araujo, S. Soares and A. L. I. Oliveria, “Hybrid morphological methodology for software development cost estimation,” Expert Systems with Applications, vol. 39, pp. 6129–6139, 2012.
[25] V. Khatibiet et al., “A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons,” Empirical Software Engineering, vol. 19, pp. 857-884, 2014.
[26] V. Khatibi et al., “A pso-based model to increase the accuracy of software development effort estimation,” Software Quality Journal, vol. 21, pp. 501-526, 2013.
[27] D. Wu, J. Li and C. Bao, “Case-based reasoning with optimized weight derived by particle swarm optimization for software effort estimation,” soft computing, vol. 22, 5299–5310, 2018.
[28] T. R. Benala and R. Mall, “DABE: Differential in Analogy-Based Software Development Effort Estimation,” Swarm and Evolutionary Computation, vol. 38, pp. 158-172, 2017.
[29] S. Ranichandra “Optimization Non-Orthogonal space distance using ACO in software cost estimation,” Mukt shabd journal, vol. IX, pp. 1592-1604, 2020.
[30] A. Khatibi, “An Intelligent Model to Predict the Development Time and Budget of Software Projects,” Int. J. Non-linear Anal. Appl. vol. 11, no. 2, pp. 85-102, 2020.
[31] A. M. Shah et al., “Ensembling artificial bee colony with analogy-Based Estimation to Improve software Development Effort Prediction,” IEE Access, vol. 8, pp. 58402-58415, 2020.
[32]A. Zakrani, M. Hain and A. Idri, “Improving Software Development effort estimating using Support Vector Regression and Feature Selection,” IAES International Journal of Artificial Intelligence, vol. 8, no. 4, pp. 399-410, 2019.
[33] E. Mendes, N. Mosley and S. Counsell, “A replicated assessment of the use of adaptation rules to improve web cost estimation,” IEEE, International Symposium on In Empirical Software Engineering, pp. 100-109, 2003.
[34] I. Angelis and I. Stamelos, “A Simulation Tool for Efficient Analogy Based Cost Estimation,” Empirical Software Engineering, vol. 5, no. 1, pp. 35-68, 2000.
[35] J. Keung, “Software development cost estimation using analogy: a review,” IEEE, Software Engineering Conference, Australian, pp. 327-336, 2009.
[36] S. K. Pal and S. C. K. Shiu, Foundations of soft case-based reasoning, John Wiley and Sons, New Jersey, 2004.
[37] J. Desharnais, “Statistical Analysis on the Productivity of Data Processing with Development Projects using the Function Point Technique,” Master's thesis, Quebec University, 1988.
[38] S. Beiranvand and M. A. Z. Chahooki, “Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques,” Journal of AI and Data Mining, vol. 4, no. 2, pp. 157-168, 2016. | ||
|
آمار تعداد مشاهده مقاله: 592 تعداد دریافت فایل اصل مقاله: 583 |
||